The Krylov subspace is a fundamental concept in numerical linear algebra, particularly useful for solving large systems of linear equations and eigenvalue problems. Given a square matrix and a vector , the -th Krylov subspace is defined as:
This subspace encapsulates the behavior of the matrix as it acts on the vector through multiple iterations. Krylov subspaces are crucial in iterative methods such as the Conjugate Gradient and GMRES (Generalized Minimal Residual) methods, as they allow for the approximation of solutions in a lower-dimensional space, which significantly reduces computational costs. By focusing on these subspaces, one can achieve effective convergence properties while maintaining numerical stability, making them a powerful tool in scientific computing and engineering applications.
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